scholarly journals Optimization of Drilling Path Using the Bees Algorithm

2021 ◽  
Author(s):  
Shafie Kamaruddin ◽  
Mohamad Naqiuddin Rosdi ◽  
Nor Aiman Sukindar
Keyword(s):  
Bees Algorithm

Fuzzy logic, neural-fuzzy network and honey bees algorithm to develop the swarm motion of aerial robots

2021 ◽  
Author(s):  
Iman Shafieenejad ◽  
Elham Dehghan Rouzi ◽  
Jamshid Sardari ◽  
Mohammad Siami Araghi ◽  
Amirhosein Esmaeili ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Honey Bees ◽  
Bees Algorithm ◽  
Aerial Robots ◽  
Fuzzy Network ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy

Engineering Problem Optimized Using Bees Algorithm

2019 ◽  
pp. 99-105
Author(s):  
Kaushik Kumar ◽  
Divya Zindani ◽  
J. Paulo Davim
Keyword(s):  
Engineering Problem ◽  
Bees Algorithm

scholarly journals A Hybrid SFL-Bees Algorithm

2015 ◽  
Vol 128 (5) ◽  
pp. 13-18
Author(s):  
Duc Hoang
Keyword(s):  
Bees Algorithm

scholarly journals Bees Algorithm for multimodal function optimisation

2015 ◽  
Vol 230 (5) ◽  
pp. 867-884 ◽  
Author(s):  
ZD Zhou ◽  
YQ Xie ◽  
DT Pham ◽  
S Kamsani ◽  
M Castellani
Keyword(s):  
Global Optimum ◽  
Bees Algorithm ◽  
Benchmark Problems ◽  
Search Technique ◽  
Local Optima ◽  
Multimodal Function ◽  
Real World Applications ◽  
Speed Up ◽  
Search Approach ◽  
Optimisation Procedure

The aim of multimodal optimisation is to find significant optima of a multimodal objective function including its global optimum. Many real-world applications are multimodal optimisation problems requiring multiple optimal solutions. The Bees Algorithm is a global optimisation procedure inspired by the foraging behaviour of honeybees. In this paper, several procedures are introduced to enhance the algorithm’s capability to find multiple optima in multimodal optimisation problems. In the proposed Bees Algorithm for multimodal optimisation, dynamic colony size is permitted to automatically adapt the search effort to different objective functions. A local search approach called balanced search technique is also proposed to speed up the algorithm. In addition, two procedures of radius estimation and optima elitism are added, to respectively enhance the Bees Algorithm’s ability to locate unevenly distributed optima, and eliminate insignificant local optima. The performance of the modified Bees Algorithm is evaluated on well-known benchmark problems, and the results are compared with those obtained by several other state-of-the-art algorithms. The results indicate that the proposed algorithm inherits excellent properties from the standard Bees Algorithm, obtaining notable efficiency for solving multimodal optimisation problems due to the introduced modifications.

Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation

Energy ◽  
2014 ◽  
Vol 71 ◽  
pp. 507-515 ◽  
Author(s):  
Hajar Bagheri Tolabi ◽  
Mohd Hasan Ali ◽  
Shahrin Bin Md Ayob ◽  
M. Rizwan
Keyword(s):  
Distributed Generation ◽  
Bees Algorithm ◽  
Multi Objective

A New Initialization Algorithm for Bees Algorithm

2013 ◽  
pp. 39-52 ◽  
Author(s):  
Wasim A. Hussein ◽  
Shahnorbanun Sahran ◽  
Siti Norul Huda Sheikh Abdullah
Keyword(s):  
Bees Algorithm ◽  
Initialization Algorithm
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